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The purpose of this paper is to evaluate the functioning of the main Spanish and Portuguese containers ports to observe if they are operating below their production capabilities.

To achieve the above-mentioned objective, one possible method is to calculate the data envelopment analysis (DEA) efficiency, and the scale efficiency (SE) of targets, and in order to consider the variability across different samples, a bootstrap scheme has been applied.

The results showed that the DEA bootstrap-based approach can not only select a suitable unit which accords with a port’s actual input capabilities, but also provides a more accurate result. The bootstrapped results indicate that all ports do not need to develop future investments to expand port infrastructure.

The proposed DEA bootstrap-based approach provides useful implications in the robust measurement of port efficiency considering different samples. The study proves the usefulness of this approach as a decision-making tool in port efficiency.

This study is one of the first studies to apply bootstrap to measure port efficiency under the background of the Spain and Portugal case. In the first stage, two models of DEA have been used to obtain the pure technical, and the technical and SE, and both the input-oriented options: constant return scale and variable return scale. In the second stage, the bootstrap method has been applied in order to determine efficiency rankings of Iberian Peninsula container ports taking into consideration different samples. Confidence interval estimates of efficiency for each port are reported. This paper provides useful insights into the application of a DEA bootstrap-based approach as a modeling tool to aid decision making in measuring port efficiency.

Describes the exploratory use of a statistical technique called data envelopment analysis (DEA), which has been widely used in other parts of the service sector, to measure the efficiency of 52 general practices in three health districts, serving 378,500 patients (78 per cent of the resident population). DEA identified over half (60 per cent) of the 52 practices as less than 100 per cent efficient, with 25 per cent potentially capable of large increases in activity without higher resource levels. There was no significant relationship between efficiency and whether partnerships were training practices, computerized, holding regular meetings, or the total list size, or age structure of practice populations except that multi‐site practices were found to be significantly more efficient as list size increased. DEA efficiency ratings were compared with a proxy for effectiveness (the ability of practices to reach target levels set in the new GP contract). Practices which were below target levels before the new contract was introduced were far less likely to be able to reach these targets once the contract was in place if they had been classified by DEA as 100 per cent efficient (i.e. judged to be unable to achieve greater outputs without increased resources). More research effort now needs to be devoted to exploring the use of DEA in measuring efficiency in general practice, and to examining the relationship between measures of efficiency and effectiveness in primary care.

The purpose of this paper is to investigate technical and scales efficiencies of MFIs in Middle East and North Africa (MENA) countries in provision of financial services. This study also aims at tracing the source of inefficiencies.

This paper uses the non‐parametric data envelopment analysis (DEA) approach to estimate the production technology for the set of MENA MFIs. The paper uses DEA because it allows us to perform analyses with small samples, which is the case for MENA, and also allows us to calculate Malmquist indexes to characterize productivity changes. Moreover, DEA does not require a production function to calculate the efficiency. It attempts to determine the efficiency of the firm against some imposed benchmark through mathematical programming.

The paper finds low technical efficiency for all MFIs under both intermediation and the production approaches of DEA methodology. This means that MFIs are wasting input resources (input oriented inefficient) and are not producing enough outputs (making loan, raising funds, and obtaining more borrowers per staff). The paper also does not find any improvement in those efficiencies during the period 2000‐2005.

The study contributes to the existing MFIs literature by pursuing an empirical and decomposition analysis of efficiency by employing two approaches of DEA methodology to trace the sources of inefficiencies which the managers, practitioners and policy makers need to focus on. DEA has been used as a tool to select the right mix of inputs and outputs to assist in tracing the sources of inefficiencies

The purpose of this paper is to explore how scale and scope of operations, firm age, and the choice to join a franchise formula influences brokerage firms' efficiency.

Four‐year data of 1,282 Dutch real estate brokerage firms is used to compute a relative efficiency measure for all firms. Consecutively, variation in this efficiency measure is explained from the firm and market characteristics.

The results show that scale and scope have a non‐linear, U‐shaped, relationship with efficiency. A reversed U‐shaped relationship is found between age and efficiency. Finally, being a member of a franchise does not necessarily lead to improved efficiency, but it depends on the franchise formula terms used.

Based on these results, managers of real estate brokerage firms are able to reconsider their own organizational design choices.

Compared to prior studies, this study uses data from multiple years. Further, the analysis also incorporates non‐linear effects of scale, scope and age on efficiency. Finally, prior research has only compared efficiency of franchise versus independent firms. This study shows that benefits of a franchise depend on the contract terms.

This paper aims to examine whether bank regulation, supervision and monitoring enhance or impede technical efficiency and risk‐taking behaviour of Islamic banks across the globe.

Technical efficiency scores are calculated using the data envelopment analysis (DEA) model while simultaneity between banks' supervision and regulation on risk and efficiency estimates are calculated using the seemingly unrelated regression (SUR) approach.

The author's results suggest that regulations and strict monitoring of banking operation, and higher supervisory power of the authorities, increase the technical efficiency for Islamic banks. The opposite effect is observed in the case of risk‐taking behaviour of Islamic banks, with higher restrictions resulting in a reduction in risk taking of Islamic banks.

The Basel II & Basel III guidelines suggested that stricter regulations and supervision could hamper banking efficiency. The existence of a powerful supervisory body could also lead to the inefficiency of banks. The DEA scores from this paper suggest that this may not necessarily be the case, especially as Islamic banks appear to be technically efficient in stricter regulatory conditions.

A message that emerges from this analysis is that there is a strong link between Islamic bank technical efficiency and risk‐taking behaviour with the Central Bank regulatory and supervisory policies. It is also conclusive that the Islamic banking system works well within a stricter regulatory environment.

This study integrates economic/ecology (eco)-attributes and performance to build a sustainable service supply chain management (SSCM) model.

This study proposes the use of the fuzzy Delphi method to screen for the less important attributes and applies a network data envelopment analysis to explore the hierarchical and eco-efficient network interrelationships. The causality and hierarchal eco-efficient model is acquired using a fuzzy decision-making trial and evaluation laboratory analysis

The findings are as follows: (1) the information and technology management process is derived by enhancing sustainable customer and supplier relationship management, and (2) the eco-efficient model is improved based on long-term relationships with suppliers – that is, synergistic suppliers improve the service chain quality and provide services in an appropriate and timely manner – and research and development coordination. The theoretical and managerial implications are discussed.

The eco-efficient model reveals that the sustainable customer relationship management process, sustainable supplier relationship management process and information and technology management process are the major causal attributes in the model.

The eco-efficient model must be based on (1) long-term relationships with suppliers, (2) synergistic suppliers to improve service chain quality, (3) the provision of services in a timely manner and (4) research and development coordination.

Prior studies neglect to build an ecological economy model using the efficiency causality model of hierarchical interrelationships. Traditional SSCM fails to involve the triple bottom line performance toward sustainability.

The aim of this analysis is to review the Indian manufacturing organizations practicing Lean Six Sigma (LSS) tools/techniques with an objective of monitoring the performance of an organization and to develop recommendation for strategies to benchmark organizational operational efficiency.

This study offers insights of the LSS performance measurement aspects of the Indian manufacturing organizations based on Data envelopment analysis (DEA) approach. The five inputs and two outputs are considered on the basis of literature review and discussed with the practitioners.

In this analysis, the relative efficiency score of 18 Indian manufacturing organizations has been determined in order to assist evaluation of the impact of monetary investment on the outputs. The present analysis not only investigates the optimum level of input variables but also lays down a significant observation that an organization having higher profit and inventory turnover ratio is not necessarily an efficient organization.

The results assist to determine the best practice units, potential source of inefficiency and deliver beneficial data for the consistent enhancement of the operational efficiency. The DEA results assist managers and decision makers to derive appropriate strategies to enhance their performance with reference to the efficient organization and to regard it as their role model.

This analysis renders a DEA based framework of LSS practicing Indian manufacturing organizations. The framework is unique in terms of its input-outputs variable selection and measurement procedure.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.

Three multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.

STC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.

This is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.